Amicrine vs Photoreceptor branch D59

library(Seurat)

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library(ggplot2)

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library(dplyr)

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library(plotly)

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library(cowplot)
library(SeuratWrappers)
library(monocle3)

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source("R:/People Folders/Connor/R_functions/Find_nCount_minimum.R")
source("R:/People Folders/Connor/R_functions/UMAP_params.R")

Here is how I have branches named now:

D59 = readRDS("D59_branches.RDS")

ggplotly(DimPlot(D59, group.by = "branch"))

Here are some cone, amicrine, and RGC markers

I notices that the cone stub does have more cone genes on than I thought, but it also blocks off

FeaturePlot(D59, c("OTX2", "CRX", "THRB", "RBP4", "TFAP2A", "POU4F2"), order = T)

FeaturePlot(D59, c("OTX2", "CRX", "THRB", "PRDM1", "TFAP2A", "POU4F2"), order = T)

FeaturePlot(D59, c("OTX2", "ATOH7", "THRB", "RBP4", "TFAP2A", "POU4F2"), order = T)

D59_3d = RunUMAP(D59, dims = 1:30, n.components = 3)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 11:14:01 UMAP embedding parameters a = 0.9922 b = 1.112

## 11:14:01 Read 3168 rows and found 30 numeric columns

## 11:14:01 Using Annoy for neighbor search, n_neighbors = 30

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## 11:14:01 Searching Annoy index using 1 thread, search_k = 3000
## 11:14:02 Annoy recall = 100%
## 11:14:02 Commencing smooth kNN distance calibration using 1 thread
## 11:14:03 Initializing from normalized Laplacian + noise
## 11:14:03 Commencing optimization for 500 epochs, with 136520 positive edges
## 11:14:11 Optimization finished

D59_3d_frame = D59_3d@meta.data

D59_3d_frame = cbind(D59_3d_frame, D59_3d@reductions$umap@cell.embeddings)

#add genes
D59_3d_frame = cbind(D59_3d_frame, t(D59_3d@assays$RNA@data[c("OTX2", "CRX", "THRB", "RBP4", "TFAP2A", "POU4F2"),]))


plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~branch, size = 1)

## No trace type specified:
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plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~OTX2, size = 1)

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plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~CRX, size = 1)

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plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~THRB, size = 1)

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##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~RBP4, size = 1)

## No trace type specified:
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plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~TFAP2A, size = 1)

## No trace type specified:
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plot_ly(D59_3d_frame, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, color = ~POU4F2, size = 1)

## No trace type specified:
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##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

##ATAC data

d53_ATAC = readRDS("../d53_ATAC_branches2.RDS")

DimPlot(d53_ATAC, group.by = "branch")

## Loading required package: Signac

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DefaultAssay(d53_ATAC) = "chromvar"

FeaturePlot(d53_ATAC, "OTX2", min.cutoff = 0)